We present a new framework for memory-efficient generative modeling. Unlike previously proposed GAN models, GON uses a single neural network to both discriminate and generate samples. This comes with the advantage of significant savings in memory footprint, but at the cost of higher training and inference times. Unlike prior work aiming to reduce the parameter size of typical generative models, this ground-up framework is pertinent for memory-constrained systems like edge devices. The training overheads would typically be acceptable in settings with (1) low sample size like few-shot or transfer learning, (2) low data dimension size or (3) time abundant settings like task scheduling or fault-tolerant edge computing where GON might execute in a few seconds, whereas decisions need to be taken every few minutes [1]}.
